His primary areas of investigation include Statistics, Estimator, Applied mathematics, Nonparametric regression and Nonparametric statistics. He regularly links together related areas like Econometrics in his Statistics studies. Many of his research projects under Estimator are closely connected to Rate of convergence with Rate of convergence, tying the diverse disciplines of science together.
His Applied mathematics research incorporates themes from Kernel, Errors-in-variables models, Nonlinear time series analysis, Mathematical optimization and Function. Wolfgang Karl Härdle works mostly in the field of Nonparametric regression, limiting it down to concerns involving Kernel regression and, occasionally, Kernel density estimation. Wolfgang Karl Härdle has researched Nonparametric statistics in several fields, including Parametric statistics, Generalized additive model, Time series, Series and Conditional expectation.
His primary scientific interests are in Econometrics, Statistics, Applied mathematics, Nonparametric statistics and Estimator. His Econometrics study frequently links to related topics such as Value at risk. His study in Statistics focuses on Nonparametric regression, Regression, Regression analysis, Smoothing and Semiparametric regression.
The various areas that he examines in his Applied mathematics study include Semiparametric model, Kernel, Linear model, Mathematical optimization and Function. His study in Parametric statistics extends to Nonparametric statistics with its themes. Specifically, his work in Estimator is concerned with the study of Asymptotic distribution.
Econometrics, Cryptocurrency, Volatility, Portfolio and Quantile are his primary areas of study. His Econometrics research is multidisciplinary, relying on both Value at risk and Systemic risk. Heteroscedasticity and Parametric model is closely connected to Skewness in his research, which is encompassed under the umbrella topic of Volatility.
His Heteroscedasticity research is multidisciplinary, incorporating perspectives in Nonparametric statistics and Kernel density estimation. In his research, Linear model is intimately related to Applied mathematics, which falls under the overarching field of Nonparametric statistics. Quantile regression is a subfield of Statistics that Wolfgang Karl Härdle tackles.
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Applied Nonparametric Regression
Nonparametric and Semiparametric Models
Comparing Nonparametric Versus Parametric Regression Fits
W. Hardle;E. Mammen.
Annals of Statistics (1993)
Smoothing Techniques: With Implementation in S
Applied Multivariate Statistical Analysis
Wolfgang Karl Härdle;Léopold Simar.
Wavelets, Approximation, and Statistical Applications
Wolfgang Härdle;Gerard Kerkyacharian;Alexander Tsybakov;Dominique Picard.
Partially Linear Models
Wolfgang Hardle;Hua LIang;Jiti Gao.
Investigating Smooth Multiple Regression by the Method of Average Derivatives
Wolfgang Härdle;Thomas M. Stoker.
Optimal Smoothing in Single-index Models
Wolfgang Härdle;Peter Hall;Hidehiko Ichimura.
Annals of Statistics (1993)
Optimal Bandwidth Selection in Nonparametric Regression Function Estimation
Wolfgang Hardle;James Stephen Marron.
Annals of Statistics (1985)
Profile was last updated on December 6th, 2021.
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